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- Deploy and implement advanced AI/ML models to tackle complex problems in target choice, patient identification, molecule design/chemistry, manufacturing and controls (CMC), and clinical trial effectiveness.
- Design and implement distributed training pipelines for LLMs using tools such as DeepSpeed, ensuring scalability and efficiency.
- Collaborate with Data Scientists in LLM customization: pre-training, fine-tuning, reinforcement learning with human feedback (RLHF), and applying parameter efficient fine-tuning (PEFT) techniques.
- Define and implement repeatable AI/ML pipelines to ensure rapid iterations of data science experiments while leveraging the cloud infrastructure to implement industry best practice in MLOps.
- Model operationalization and monitoring on Azure and GCP infrastructures with close collaboration with the DevSecOps team.
- Build scalable, reusable backend systems including the state-of-the-art agentic frameworks to support GenAI products across R\&D functions. Develop robust logging, telemetry, and evaluation harnesses to ensure reliable model performance.
- Technology assessment of external product solutions and co-development with strategic partners
- Create and maintain pipelines for producing training/testing/validation data sets
- Build and maintain model evaluation framework
Why You?
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BS degree in computer science, engineering, bioinformatics or applied math
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5 years of engineering experience
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Experience working with LLM technologies, including GenAI embedding techniques, modern model architecture, retrieval-augmented generation (RAG), fine tuning/pre-training AI models, and evaluation benchmarks
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Experience in Python, TensorFlow/PyTorch, and scalable ML architectures.
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Experience in Agentic framework, i.e., AutoGen and LangGraph
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Experience in Azure and GCP cloud services
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Experience in full stack software development, and knowledge of software engineering principles around testing, code reviews and deployment.
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MA degree in computer science or engineering
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10 years of combined full stack, data engineering, Azure/GCP cloud services, and AI engineering experience
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Experience with graph databases in the context of GraphRAG
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Experience with data lake-house architecture, data catalog, master data management applications
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Experience in establishing AI/ML and agent best practices, standards, and ethics
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Experience in AI/ML applications in life science domain areas: pre-clinical research, clinical trial design and operation, precision medicine, regulatory science, and CMC.
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Experience in reducing the cost of running a service / capability while maintaining or improving performance efficiencies
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Strong analytical and problem-solving skills, with a passion for shaping AI-driven workflow.
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Strong written and verbal communication skills
Why GSK? Uniting science, technology and talent to get ahead of disease together.
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